Dynamic sequential box modelling of inhalation exposure potential in multi-bed patient ward: Validation and baseline case studies

Macro-models provide a fast simulation tool for exposure assessments and design of control interventions, yet there are concerns about their accuracies. Single-zone based models are known to be inadequate for predicting exposure to near-source emissions, while the complexity of sequential box models (SBM) limits their application to steady-state conditions. However, treating unsteady conditions as steady underestimates peak inhalation exposure potentials, especially near the source. This study employs a sub-configuration validation approach to validate the unsteady SBM, using an R open-source-based numerical solver, for estimating indoor pollutant concentration, exposure and/or infection risks. The validated model is used to assess the performance of ASHRAE S170-2017 baseline specifications for inhalation exposure control in a multi-bed patient ward with air recirculation. In the baseline studies, quanta infective concentration and reference concentration were respectively used as source strength and threshold values for influenza pathogen. Robust design methodology was employed in the experimental design and analysis of the control and noise factors. Results indicate a close relationship between SBM and the sub-configuration validation datasets. Findings also show that concentration gradients exist in SBM with the highest values in the near-field zones. Thus, with SBM, the well-mixed assumption does not necessarily imply equal exposure potentials. Robustness analysis shows that stratum ventilation is three-fold insensitive to the variability in exposure than mixing ventilation. Finally, the results of the case studies revealed that the average inhalation exposure exceeds the influenza reference concentration, thereby suggesting an insufficiency of the baseline conditions to offer protection against inhalation exposure to influenza contagion.